This paper proposes the spherical harmonic and the cylindrical harmonic decompositions of the acoustic velocity vectors of the outgoing sound field. The spherical harmonic coefficients and the cylindrical harmonic coefficients of the pressure of the outgoing sound field can be measured by a spherical microphone array and a cylindrical microphone array surrounding the source, respectively. By using the sound field translation formula, the spherical harmonic coefficients and the cylindrical harmonic coefficients of the acoustic velocity vectors of the outgoing sound field are obtained. Simulations show the proposed spherical harmonic and cylindrical harmonic decompositions of the acoustic velocity vectors can accurately model a monopole, a dipole and a quadrupole. The proposed decompositions can be used in exterior noise control, where an array of loudspeakers controls the acoustic velocity vectors of the outgoing sound field.
In environments with competing sound sources, speech intelligibility can be significantly compromised. This paper addresses the near-end listening enhancement (NELE) problem, i.e., the problem of processing an available clean speech signal in order to maximize its intelligibility when it is subsequently presented to a human listener in an adverse acoustic situation. We propose a time-invariant and low-complexity NELE algorithm that maximizes an approximation of the Speech Intelligibility Index by redistributing speech energy across frequency bands. Unlike existing algorithms, the proposed algorithm incorporates a mechanism that allows it to distinguish between temporally fluctuating and non-fluctuating noise maskers by using only long-term speech and noise statistics. Simulation results show that the proposed method outperforms baseline algorithms, whether time-invariant or time-varying, in a wide range of noise conditions.
In this paper, we introduce a post-processing method to minimize the algorithmic latency in traditional blind source separation (BSS) techniques. Our proposed approach involves the incorporation of a minimum variance distortion response method with the spatial covariance matrix, which is derived from conventional BSS methods, to effectively compute short demixing filters. The performance of source separation can be improved either by increasing the number of microphones or by integrating a dereverberation technique as a pre-processing step, or even both. The experimental results confirm the effectiveness and consistency of the proposed approaches on diverse speech databases.
The perceptual performance of active noise control (ANC) head-phones depends on various sound attributes that collectively influence end-user satisfaction. In the speech prediction-based ANC for headphone applications, two key attributes, the amount of speech and the amount of artifacts, significantly affect subjective satisfaction with the ANC experience. This study aims to identify objective metrics correlating with the perceptual assessment of these attributes to predict subjective satisfaction with the considered ANC systems. Such prediction can significantly simplify the design and evaluation process of the ANC systems, avoiding expensive and time-consuming subjective tests. By establishing the perceptual relevance of attenuation, a commonly used objective performance metric for ANC, to the amount of speech and utilizing conventional quality metrics, i.e., PEAQ, POLQA, and PESQ, for the amount of artifacts, we demonstrate high-quality fits when predicting these attributes. Our findings indicate that these metrics effectively predict subjective satisfaction with the considered ANC systems, as evidenced by a high explained variance, with an R-squared of 0.98 and a relatively low root mean square error ranging from 0.09 to 0.19 on a five-point scale.
Hearing aids, and more generally embedded devices, have undergone significant evolution, transitioning into devices capable of significant compute on-device. However, next generation speech processing also utilises computationally expensive deep neural networks. Therefore, it is often preferable to execute the high demanding parts at a central, more powerful device. Certainly for localisation, which has less strict latency requirements. This however requires wireless data transmission between the on ear devices and the central unit, which can adversely impact battery life. In this work, we compare different strategies for bandwidth efficient deep localisation. One strategy is to send the audio signals directly to the central device, and use audio codecs, like the LC3plus codec to minimise the bandwidth. An alternative method is to adapt the co-operative localisation method to binaural hearing and investigate methods to reduce its bandwidth. The co-operative model first processes the microphone signals locally, before transmitting features to the central processor for further analysis. We investigate quantisation, time compression and lowering the dimension of these features. The cooperative model proved slightly better at high SNR scenarios, while the audio transmission model at low SNR cases.
